Snippets of system design, good practices, and tradeoffs
for orchestrating AI and ML workloads in an enterprise environment.

• Design choices for enterprise data platform
• Deploying ML model to production

• Establishing agentic mesh
• Integrating a company context into enterprise chatbot
• Fine-tuning vs RAG vs prompt engineering tradeoffs
• Validating models and selecting right KPIs
• Adding observability for agents
• Leveraging managed AI Platforms (GCP Vertex AI, Azure AI, AWS SageMaker/Bedrock)
• From research to production: lessons from self-driving cars industry